DIPPER Lab at KNUST, Ghana
👨🎓 Andrews Tang is a passionate computer engineering researcher from Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. With a deep interest in deep learning and computer vision, Andrews has worked on impactful projects in areas such as agricultural quality control, food safety, and aviation safety. His work, which includes the development of deep learning models for detecting red palm oil adulteration and tomato condition assessment, aims to address critical challenges in the African context. He is a recipient of multiple academic honors, including the Excellent Student’s Award and has presented at international conferences such as AfricAI and Deep Learning Indaba. Andrews also contributes as a teaching assistant and mentor in his field, shaping the next generation of computer engineering students. 🚀
Publication Profile :
Google Scholar
🎓 Educational Background :
🎓 Bachelor of Science in Computer Engineering
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana (2018-2022)
• First Class Honours
• Cumulative Weighted Average (CWA): 76.18%
• Final CGPA: 3.74/4.0 (WES Evaluation)
💼 Professional Experience :
Andrews Tang has a strong academic foundation and extensive research experience at KNUST, where he has worked on groundbreaking projects with the DIPPER Lab and Responsible AI Lab (RAIL). As an Undergraduate Researcher and Research Assistant, he has tackled diverse challenges, from designing a decentralized food traceability system for Ghana’s agricultural supply chain to developing innovative deep learning models for detecting palm oil adulteration using GhostNet and SqueezeNet. In the field of Aviation Safety, Andrews is currently working as a Machine Learning Engineer, focusing on enhancing the accuracy of the Instrument Landing System (ILS) for low-visibility conditions, where his predictive models have improved flight safety protocols. His other work includes contributions to EEG report classification, sign language recognition, and mineral ore recovery predictions. Alongside his technical expertise, Andrews actively participates in mentorship and teaching roles, providing guidance in computer vision and secure network systems to undergraduate students at KNUST.
🔍 Deep Learning
📷 Computer Vision
🧠 AI in Agriculture and Food Safety
🌐 Blockchain in IoT
✈️ Machine Learning for Aviation Safety
Awards & Honors:
🏆 Excellent Student’s Award (2020, 2022, 2023)
🏆 Best Poster Award, Deep Learning Indaba, Accra, 2023
🌍 Member, Black in AI Fellowship, 2024
📝 Publication Top Notes :
- Tchao, E. T., Gyabeng, E. M., Tang, A., Benyin, J. B. N., Keelson, E., & Kponyo, J. J. (2022). “An Open and Fully Decentralized Platform for Safe Food Traceability.” 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 487-493.
DOI: 10.1109/CSCI58124.2022.00092
- Tang, A., Agbemenu, A. S., Tchao, E. T., Keelson, E., Klogo, G. S., & Kponyo, J. J. (2024). “Assessing Blockchain and IoT Technologies for Agricultural Food Supply Chains in Africa: A Feasibility Analysis.” Heliyon, 10(4), e34584.
DOI: 10.1016/j.heliyon.2024.e34584
- Gyabeng, E. M., Tang, A., Agbemenu, A. S., Zaukuu, J. Z., Keelson, E., & Tchao, E. T. (2024). “AfroPALM – Afrocentric Palm Oil Adulteration Learning Models: An End-to-End Deep Learning Approach for Detection of Palm Oil Adulteration in West Africa.” LWT – Journal of Food Science and Technology. Preprint available at SSRN:
https://ssrn.com/abstract=4917970
Revised Manuscript Under Review By Elsevier LWT Journal.